MIT SMR’s 2018 Strategic Measurement study reveals how organizations using machine learning to enhance KPI-driven decision-making are pulling ahead of their competitors.
Machine learning (ML) is changing how leaders use metrics to drive business performance, customer experience, and growth. A small but growing group of companies is investing in ML to augment strategic decision-making with key performance indicators (KPIs). Our research,1 based on a global survey and more than a dozen interviews with executives and academics, suggests that ML is literally, and figuratively, redefining how businesses create and measure value.
KPIs traditionally have had a retrospective, reporting bias, but by surfacing hidden variables that anticipate “key performance,” machine learning is making KPIs more predictive and prescriptive. With more forward-looking KPIs, progressive leaders can treat strategic measures as high-octane data fuel for training machine-learning algorithms to optimize business processes. Our survey and interviews suggest that this flip ― transforming KPIs from analytic outputs to data inputs ― is at an early, albeit promising, stage.
Those companies that are already taking action on machine learning ― investing in ML and actively using it to engage customers ― differ radically from companies that are not yet investing in ML. They are far more likely to:
- Develop a single, integrated view of their target customer.
- Have the ability to drill down to see underlying KPI data.
- Check their KPI reports frequently.
These differences all depend on treating data as a valuable corporate asset. We see a strong correlation between companies that embrace ML and data-driven decision-making.
Augmenting Execution With Machine Learning
Nearly three quarters of survey respondents believe their organization’s current functional KPIs would be better achieved with greater investment in automation and machine-learning technologies. Our interviews with senior executives identified a variety of innovative ML practices. Without exception, the companies with the most intriguing and ambitious ML initiatives were the ones with the most serious commitment ― cultural and organizational ― to managing data as a valuable corporate asset.